Assessment of building damages and adaptation options under extreme flood scenarios in Shanghai

Plenty of various measures have been taken to mitigate flood losses in Shanghai over thousands of years, including the construction of sea dikes and floodwalls. However, the combined effects of intensified rainstorms, sea-level rise, land subsidence, and rapid urbanization are exacerbating extreme flood risks and potential flood losses in the fast-developing coastal city. In light of these changes, this article presents an assessment of possible exposure and damage losses of buildings in Shanghai (including residential, commercial, workplace, and industrial buildings). Based on extreme flood scenarios caused by storm surges, precipitation, and fluvial floods, current flood-defence standards will soon be overtaken. Further analyses show that the inundation area could reach 9 %, 16 %, 24 %, and 49 % of Shanghai (excluding the area of islands) under the 1/200, 1/500, 1/1000, and 1/5000-year flooding scenarios, respectively. This study finds, in terms of the total building damage, the 1/5000-year flood scenario damage is more than ten times the 1/200-year flood scenario. Accordingly, the average annual loss (AAL) of residential, commercial, office, and industrial buildings are 13.9, 2.3, 5.3, and 3.9 million USD. Specifically, among the 15 (non-island) districts in Shanghai, Pudong has the highest exposure and AAL at all the four flood scenarios, while the inner city (including seven districts) is also subject to extreme AAL of up to 40 % of its total building values. This study further addresses the possibilities of these extreme flood scenarios, and adaptation options such as: strategic urban planning, advanced building protections, and systematic flood management. Conclusions of the study provide information for scenario-based decision making and cost-benefit analysis for extreme flood risk management in Shanghai and is applicable to other similar coastal megacities.

Uncertainty quantification and attribution in flood risk assessment using Global Flood Models: an application to the river Rhine basin

<p>Floods are extreme hydro-meteorological hazards that pose significant risks to the economy and society. Reducing the risk associated with floods and better adapting to them is a daunting task because flood risk dynamics are influenced by different factors. Flood risk is usually defined as the product of three components: hazard, exposure and vulnerability. Global Flood Risk Models (GFRM) represent the underlying physical hazard, the exposure of people, properties or other assets to the hazard, and the losses that may occur following a flood event.  Consequently, they are used by governmental agencies, risk reduction organisations, global investors and the (re)insurance industry to help manage the societal and financial risks associated with floods. GFRMs are subject to many sources of uncertainty, including uncertainty in processes representation, model parameters and input data; however, the relative importance of these different sources is poorly understood. Currently, no evidence exists on which uncertain input factor mostly control the final uncertainty in predicted losses in different places and circumstances. In this project, we use JBA’s (a leading flood risk modelling company) Global Flood Model and Open Exposure Data (OED) to develop an appropriate methodological approach to analyse the sensitivity of loss predictions in a structured way. This is particularly challenging as input uncertainties exhibit complex spatially distributed and spatially-structured (correlated) patterns. We apply the methodology to the Rhine river basin, covering regions with different physical and socio-economic characteristics. We pursue the following objectives; (1) Identify and quantify the various sources of uncertainty e.g. associated to rainfall data, extraction of flood events sets, defence database, vulnerability curves, exposure portfolios (2) Analyse their relative importance on flood losses predictions across places along the river (3) Understand which of them are most important at each place. We aim to produce scientifically robust evidence about the importance of different sources of uncertainty across places with different climate, hydrology and socio-economic characteristics and try to address questions related to exposure and vulnerability dynamics, flood losses modelling and adaptation strategies. Such evidence base will help prioritise efforts for uncertainty reduction of the case study model, as well as other flood risk models used by (re)insurers and government agencies, ultimately contributing to more informed decisions for flood risk mitigation.</p>

A global analysis of the interplay between flood severity ad human dynamics in floodplains

<p>This study aims at exploring whether changes in the spatial distribution of the human population and the built-up areas within floodplains can be associated with extreme flood events generating severe economic losses and fatalities. We relate economic losses and fatalities caused by floods during 1990‐2000, with changes in human population and built‐up areas in floodplains during 2000‐2015 by exploiting global archives as the Global Human Settlement, GFPLAIN250m, and the EM-DAT datasets. Despite the frequent flood losses in the previous period 1990‐2000, we found that population and built‐up areas in floodplains increased in the period 2000‐2015 for the majority of the analyzed countries. On the other hand, we observed a reduction in floodplains population after more severe flood losses that occurred in the period 1975‐2000. Finally, floodplains population increased after a period of high flood fatalities in low‐income countries, while built‐up areas increased after a period of frequent economic losses in upper‐middle and high‐income countries. This study can be used as a general framework for advancing knowledge of human‐flood interactions and support the development of sustainable policies and measures for flood risk management and disaster risk reduction.</p>

A probabilistic approach to estimating residential losses from different flood types

Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model’s ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model’s performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.

Flood risk assessment and scenarios for a flood insurance program in central Vietnam EGU2018

Severe floods occur in several coastal area of Vietnam and causes both properties and causalities. It is important to guide local households in selecting risk management strategies according to their capacity to reduce significant damage (Molinari et al). Insurance was recently recognized as one of the possible non structural measures to mitigate losses and damages from natural disasters (UNISDR, 2015). Despite the huge effect on citizens' life from flood risk and believable higher damages caused by flood in the future due to climate change, it is surprising that there is no flood insurance program in Vietnam. This paper investigates the vulnerability of households to flood and suggests a conceptual framework to implement possible scenarios of flood insurance programs for Central Vietnam. We implement 2D simulations of the physical hazard in term of flooding, velocity, depth and duration, and then combined the flood hazard maps, exposure and vulnerability maps to obtain risk maps. We calculate major flood damages, regarding housing damages and paddy rice damages, for five different return periods (10, 20, 50, 100, 200 years). Based on 386, out of 400 responses to 35 questions in a questionnaire directly related to flood risk and preparedness, the vulnerability curve for direct tangible costs to building structure, building contents, and paddy rice were assessed. We also find that, in the long term, with a proper flood insurance premium, three actors (Vietnam Government, insurance companies, and local households) could all achieve their targets. Local households are insured by insurance contracts against flood losses with a reasonable insurance premium. Vietnam Government might reduce its budget for subsidising local households after flooding by transferring flood risk to insurance companies. Insurance companies might make profits in long term as this market has a potential growth in Vietnam. One more important contribution of this paper is that this measure could guide land planning toward decisions which tend to minimize the total flood losses